Note: The job is a remote job and is open to candidates in USA. MeridianLink is a company focused on building customer-facing AI capabilities for credit unions. They are seeking a Staff Software Engineer to lead the technical direction of their AI platform, focusing on architecture, strategy, and hands-on delivery while collaborating with multiple engineering teams.
Responsibilities
- Own the reference architecture for the AI platform, encompassing API abstractions, prompt and version management, RAG infrastructure, vector retrieval, evaluation harnesses, and model serving
- Lead build-vs-buy evaluations for AI components, including due diligence and integration planning
- Design foundational data pipelines and infrastructure to support reliable, scalable AI services
- Define and document API contracts, standards, and platform interfaces across engineering
- Contribute to backend development in Python and RESTful API design, including integrations with leading AI providers
- Participate in full-stack delivery when needed, using React/TypeScript/Vite to deliver AI features
- Build and maintain tooling, SDKs, and platform APIs to enable safe, scalable AI adoption by product teams
- Drive improvements to AI-assisted development workflows with tools like GitHub Copilot and Claude
- Implement integrations with LLMs: context management, prompt templating, response validation, and robust fallback strategies
- Build and maintain evaluation frameworks for reproducible model testing
- Establish observability and performance tracking for AI services
- Participate in designing vector-based retrieval and model serving infrastructure as custom model capabilities become a focus
- Advise and collaborate with Security through architecture and design phases, ensuring compliance and security standards are met
- Apply secure-by-default principles (encryption, least privilege, audit logging) and stay current with financial data governance and privacy standards
- Collaborate with Product to translate business needs into technical designs and specifications, while contributing a strong architectural perspective
- Support AI platform roadmap development with product leadership
- Engage in companywide architectural forums and workgroups to drive engineering standards
- Partner with Data Engineering, DevOps/SRE, and Security to ensure aligned technical foundations
- Mentor Senior engineers through code reviews, design sessions, and architectural guidance
- Promote engineering excellence, robust documentation, and reusable internal resources
Skills
- 8+ years in professional software engineering, with demonstrated cross-team architectural influence
- Proven end-to-end delivery of AI-powered features, from product concepts to production
- Experience leading large, complex technical initiatives
- Strong proficiency in Python and RESTful API development (FastAPI, Django)
- Knowledge of full-stack development (React, TypeScript)
- Hands-on integration with third-party LLM/AI APIs
- Experience with AWS (preferred), including IAM, networking, managed services, and storage
- Solid foundation in distributed systems, API design, cloud-native architecture
- Comfortable with CI/CD pipelines, version control, Docker, and AI-assisted development tools
- Demonstrated ability to author durable technical proposals (RFCs, ADRs)
- Clear communicator with technical and non-technical stakeholders
- GenAI experience (e.g., RAG pipelines, prompt management, LLM evaluation, agent frameworks)
- Familiarity with vector databases/semantic search (Pinecone, pgvector, OpenSearch)
- Exposure to model serving, MLOps/LLMOps practices, and model lifecycle/deployment tools
- Experience building developer platforms, SDKs, and self-service engineering tooling
- Experience with IaC tools (Terraform/Pulumi), Kubernetes, and observability tools (Prometheus, Grafana, Datadog)
- Leadership in technical communities, mentoring, and architectural standards
- Bachelor's in Computer Science, Software Engineering, or equivalent experience
- Experience building in financial services, fintech, or regulated environments
- Working familiarity with AI governance in financial services (e.g., NCUA, model risk, fair lending)
- Understanding of SOC 2 or similar compliance frameworks from an engineering perspective
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